Database Commons

a catalog of worldwide biological databases

Profiles of Chemical Effects on Cells (pCEC) is a toxicogenomics database with a system of classifying chemicals that have effects on human health. This database stores and handles gene expression profiling information and categories of toxicity data. Chemicals are classified according to the specific tissues and cells they affect, the gene expression changes they induce, their toxicity and biological functions in this database system. The pCEC system also analyzes relationships between chemicals and the genes they affect in specific tissues and cells. The reason why we developed pCEC is to support decision-making within the context of environmental regulation. Especially, exposure to environmental chemicals during fetal and newborn development may result in a predisposition to various disorders such as cancer, learning disabilities and allergies later in life. The identification and prediction of hazardous chemicals using limited information are important issues in human health risk management. Therefore, various toxicity information including lethal dose 50 (LD50), toxicity pathways and pathological data were loaded into pCEC. pCEC is also a facility for query, analysis and prediction of unknown toxicochemical reaction pathways and biomarkers which are based on toxicoinformatical data mining approaches. This database is available online at http://project.nies.go.jp/eCA/cgi-bin/index.cgi. The current version of the database has information on the hepatotoxicity, reproductive toxicity and embryotoxicity of chemicals.

Environmental stressors such as chemicals and physical agents induce various oxidative stresses and affect human health. To elucidate their underlying mechanisms, etiology and risk, analyses of gene expression signatures in environmental stress-induced human diseases, including neuronal disorders, cancer and diabetes, are crucially important. Recent studies have clarified oxidative stress-induced signaling pathways in human and experimental animals. These pathways are classifiable into several categories: reactive oxygen species (ROS) metabolism and antioxidant defenses, p53 pathway signaling, nitric oxide (NO) signaling pathway, hypoxia signaling, transforming growth factor (TGF)-beta bone morphogenetic protein (BMP) signaling, tumor necrosis factor (TNF) ligand-receptor signaling, and mitochondrial function. This review describes the gene expression signatures through which environmental stressors induce oxidative stress and regulate signal transduction pathways in rodent and human tissues.

Gene clustering is one of the main themes of data mining approaches in bioinformatics. Although it has the power to analyze gene function, interpretation of the results becomes increasingly difficult when the number of experiments (samples) exceeds hundreds or more. A new type of clustering called "biclustering," where genes and experiments are coclustered in a large-scale of gene expression data, has been extensively studied in the last decade. We have developed "SAMURAI," an original program that detects all the biclusters or "gene modules" whose genes have similar expression patterns to query profile using the ultrafast data mining algorithm called Linear-time Closed itemset Miner (LCM). Using chemical toxicity dataset from J&J rat liver experiments, we compiled an exhaustive dictionary of gene modules by searching datasets of gene modules with each chemical exposure experiment as query. Through the module analysis, we found that our program can detect up/down-regulated gene sets that significantly represent particular GO functions or KEGG pathways, thereby unraveling reactions and mechanisms common to different toxicochemical treatments of hepatocytes.